Object recognition is a key feature for building robots capable of moving and performing tasks in human environments. However, current object recognition research largely ignores the problems that the mobile robots context introduces. This work addresses the problem of applying these techniques to mobile robotics in a typical household scenario. We select two state-of-the-art object recognition methods, which are suitable to be adapted to mobile robots, and we evaluate them on a challenging dataset of typical household objects that caters to these requirements. The different advantages and drawbacks found for each method are highlighted, and some ideas for extending them are proposed. Evaluation is done comparing the number of detected objects and false positives for both approaches.
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